In addition to SIP, we also ran a simple enrichment experiment, adding one 12C substrate to smaller microcoms containing ~ 5g soil. Unlike the SIP experiment, only one substrate was added to each microcosm and this time we used soil from all 10 locations, not just Monkey Run. Also we only sampled microcoms on two days for each substrate:
We also had water treated controls for each timepoint as something to compare against.
In this notebook I will examine these samples both on their own and in relation to the incorporators identified in the MW-HR-SIP analysis.
# For data handling
library(dplyr)
library(phyloseq)
# For analysis
library(vegan)
library(nlme)
library(lsmeans)
# For plotting
library(ggplot2)
# Set color schemes
eco.col = c(agriculture="#00BA38", meadow="#619CFF", forest="#F8766D")
g_legend<-function(a.gplot){
tmp <- ggplot_gtable(ggplot_build(a.gplot))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend <- tmp$grobs[[leg]]
return(legend)}
The OTU abundances and phylogeny can be found in the master phyloseq object.
# Import phyloseq object
physeq = readRDS("/Users/sambarnett/Documents/Buckley Lab/FullCyc2/fullcyc2_backups_8_8_19/phyloseq/fullcyc2physeq.RDS")
# Subset to just the unfractionated samples and remove the controls
enr.physeq = subset_samples(physeq, exp_type == "Enrichment" & sample_type == "unknown")
bulk.physeq = subset_samples(physeq, exp_type == "bulk" & sample_type == "unknown")
physeq = NULL
# Remove non-bacteria (aka Archaea)
enr.physeq = subset_taxa(enr.physeq, Domain == "Bacteria")
bulk.physeq = subset_taxa(bulk.physeq, Domain == "Bacteria")
# Add in a different phylogenetic tree. The one in the phyloseq might be an older version.
tree = read_tree("/Users/sambarnett/Documents/Buckley Lab/FullCyc2/fullcyc2_backups_8_8_19/fullcyc2.bacteria.cogent.tree")
phy_tree(enr.physeq) = tree
phy_tree(bulk.physeq) = tree
# Remove any OTUs no longer found in the samples
enr.physeq = prune_taxa(taxa_sums(enr.physeq) > 0, enr.physeq)
bulk.physeq = prune_taxa(taxa_sums(bulk.physeq) > 0, bulk.physeq)
Incorporator status can be found in the log2 fold change dataframe.
# Get the l2fc dataframe and add in columns indicating the land-use, labeled substrate, and day
l2fc.df = readRDS(file = "/Users/sambarnett/Documents/Buckley Lab/FullCyc2/fullcyc2_l2fc_testoutput.rds") %>%
mutate(ecosystem = factor(gsub(".+ecosystem == [ \']*([A-z]+).+", "\\1", .id),
levels = c("agriculture", "meadow", "forest")),
day = gsub(".+day == [ \']*([0-9]+).+", "\\1", .id),
substrate = factor(gsub(".+(13C-[A-z]+).+", "\\1", .id),
levels = c("13C-Xyl", "13C-Ami", "13C-Van", "13C-Cel", "13C-Pal")))
For many of the following analyses I want a rarefied OTU table. This is one way to correct for differing sequencing depths across all my samples. I will set the seed for this process so that I can replicate this analysis if necessary (seed = 4242).
enr.rare.physeq = rarefy_even_depth(enr.physeq, rngseed=4242)
bulk.rare.physeq = rarefy_even_depth(bulk.physeq, rngseed=4242)
unique(colSums(otu_table(enr.rare.physeq)))
## [1] 14503
print(paste("Maximum read count =", max(colSums(otu_table(enr.physeq)))))
## [1] "Maximum read count = 194737"
print(paste("Minimum read count =", min(colSums(otu_table(enr.physeq)))))
## [1] "Minimum read count = 14503"
print(paste("Rarefied read count =", unique(colSums(otu_table(enr.rare.physeq)))))
## [1] "Rarefied read count = 14503"
print(paste("Number of OTUs total =", ntaxa(enr.physeq)))
## [1] "Number of OTUs total = 28296"
print(paste("Number of OTUs rarefied =", ntaxa(enr.rare.physeq)))
## [1] "Number of OTUs rarefied = 24659"
print(paste("Number of phyla total =", length(unique(filter(data.frame(tax_table(enr.physeq), stringsAsFactors = FALSE), !(is.na(Phylum)))$Phylum))))
## [1] "Number of phyla total = 45"
print(paste("Number of phyla total =", length(unique(filter(data.frame(tax_table(enr.rare.physeq), stringsAsFactors = FALSE), !(is.na(Phylum)))$Phylum))))
## [1] "Number of phyla total = 45"
As with the SIP enrichments I want to see if there carbon addition leads to a shift in diversity.
Here I’ll calculate the desired alpha diveristy measures.
OTU.table = t(otu_table(enr.rare.physeq))
alpha_div.df = data.frame(X.Sample = rownames(OTU.table),
richness = specnumber(OTU.table),
shannon = diversity(OTU.table, index="shannon"),
simpson = diversity(OTU.table, index="simpson")) %>%
mutate(evenness = shannon/log(richness)) %>%
left_join(data.frame(sample_data(enr.rare.physeq)) %>% select(X.Sample, ecosystem, substrate, day, location),
by = "X.Sample") %>%
mutate(ecosystem = factor(ecosystem, levels = c("agriculture", "meadow", "forest")),
substrate = factor(substrate, levels = c("H2O-Con", "12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
time_point = ifelse(day %in% c(2, 14), "Early", "Late")) %>%
tidyr::gather(key="method", value="Treatment",
-X.Sample, -ecosystem, -substrate, -day, -location, -time_point)
alpha_div_H2O.df = alpha_div.df %>%
filter(substrate == "H2O-Con") %>%
rename(Control = Treatment) %>%
select(ecosystem, location, day, method, Control)
alpha_div_treat.df = alpha_div.df %>%
filter(substrate != "H2O-Con") %>%
select(-X.Sample) %>%
left_join(alpha_div_H2O.df, by = c("ecosystem", "location", "day", "method")) %>%
tidyr::gather(key = "treatment", value = "diversity", -ecosystem, -substrate, -day, -location, -time_point, -method) %>%
arrange(ecosystem, substrate, day, location)
Early timepoints are the first timepoint for each of the substrates (Day 2 for Xylose, amino acids, and vanillin; Day 14 for palmitic acid and cellulose). This is similar to the early timepoints for SIP and likely captures bacterial growth due to substrate addition more so than secondary feeding.
## Get bulk
bulk_meta.df = data.frame(sample_data(bulk.rare.physeq)) %>%
select(location, ecosystem, pH, organic_content_perc, DNA_conc__ng_ul) %>%
mutate(DNA_conc__ng_ul = as.numeric(as.character(DNA_conc__ng_ul)))
evenness_early_meta.df = alpha_div_treat.df %>%
filter(method == "evenness", time_point == "Early") %>%
tidyr::spread(key=treatment, value=diversity) %>%
mutate(delta_evenness = Treatment-Control) %>%
left_join(bulk_meta.df, by = c("ecosystem", "location")) %>%
mutate(ecosystem = factor(ecosystem, levels=c("agriculture", "meadow", "forest")))
## Compare to zero
early_evenness_wilcox.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
model = wilcox.test(x = filter(evenness_early_meta.df, substrate==sub)$delta_evenness, alternative = "less", mu=0)
model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
Vstat = model$statistic,
pvalue = model$p.value)
early_evenness_wilcox.df = rbind(early_evenness_wilcox.df, model.df)
}
early_evenness_wilcox.df = early_evenness_wilcox.df %>%
mutate(padj = p.adjust(pvalue, method = "BH", n = 5)) %>%
mutate(sig = ifelse(padj < 0.001, "***",
ifelse(padj < 0.01, "**",
ifelse(padj < 0.05, "*", "NS"))))
early_evenness_wilcox.plot = ggplot(data=evenness_early_meta.df, aes(x=substrate, y=delta_evenness)) +
geom_hline(yintercept = 0, linetype=2, color="red") +
geom_boxplot(outlier.shape = NA) +
geom_jitter(aes(color=ecosystem), alpha=0.5) +
geom_text(data=filter(early_evenness_wilcox.df, padj < 0.05), aes(x=substrate, label=sig), y=0.06, size=6) +
scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
labs(x="Substrate", y="Change in evenness", color="Land-use") +
lims(y=c(-0.3, 0.1)) +
theme_bw() +
theme(axis.text.x = element_text(size=12, angle=45, vjust=1, hjust=1),
axis.text.y = element_text(size=12),
axis.title = element_text(size=12),
strip.text = element_text(size=12),
legend.text = element_text(size=12),
legend.title = element_text(size=12),
legend.position = "top")
## Compare with organic content
early_SOM_evenness.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
sub.evenness.df = filter(evenness_early_meta.df, substrate==sub)
cor.res = cor.test(x=sub.evenness.df$organic_content_perc, y=sub.evenness.df$delta_evenness)
model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
r = cor.res$estimate,
pvalue = cor.res$p.value) %>%
mutate(padj = p.adjust(pvalue, method = "BH", n = 5))
early_SOM_evenness.df = rbind(early_SOM_evenness.df, model.df)
}
early_evenness_SOM.plot = ggplot(data=evenness_early_meta.df, aes(x=organic_content_perc, y=delta_evenness)) +
geom_smooth(method="lm", se=FALSE, color="grey70", size=0.75) +
geom_point(aes(color=ecosystem)) +
geom_text(data=early_SOM_evenness.df, aes(label=paste("r==", round(r, 3), sep="")),
x=0.03, y=-0.2, hjust=0, parse = TRUE) +
geom_text(data=early_SOM_evenness.df, aes(label=paste("p==", round(padj, 3), sep="")),
x=0.03, y=-0.235, hjust=0, parse = TRUE) +
scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
lims(y=c(-0.3, 0.1)) +
labs(x="% SOM in bulk soil", y="Change in evenness", color="Land-use") +
theme_bw() +
theme(axis.text.x = element_text(size=12, angle=90, vjust=0.5),
axis.text.y = element_text(size=12),
axis.title = element_text(size=12),
strip.text = element_text(size=12),
legend.text = element_text(size=12),
legend.title = element_text(size=12),
legend.position = "top") +
facet_wrap(~factor(gsub("12C-", "", substrate), levels=c("Xyl", "Ami", "Van", "Cel", "Pal")), nrow=1)
## Compare with DNA concentration
early_DNA_evenness.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
sub.evenness.df = filter(evenness_early_meta.df, substrate==sub)
cor.res = cor.test(x=sub.evenness.df$DNA_conc__ng_ul, y=sub.evenness.df$delta_evenness)
model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
r = cor.res$estimate,
pvalue = cor.res$p.value) %>%
mutate(padj = p.adjust(pvalue, method = "BH", n = 5))
early_DNA_evenness.df = rbind(early_DNA_evenness.df, model.df)
}
early_evenness_DNA.plot = ggplot(data=evenness_early_meta.df, aes(x=DNA_conc__ng_ul, y=delta_evenness)) +
geom_smooth(method="lm", se=FALSE, color="grey70", size=0.75) +
geom_point(aes(color=ecosystem)) +
geom_text(data=early_DNA_evenness.df, aes(label=paste("r==", round(r, 3), sep="")),
x=5, y=-0.2, hjust=0, parse = TRUE) +
geom_text(data=early_DNA_evenness.df, aes(label=paste("p==", round(padj, 3), sep="")),
x=5, y=-0.235, hjust=0, parse = TRUE) +
scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
lims(y=c(-0.3, 0.1)) +
labs(x="DNA Concentration", y="Change in evenness", color="Land-use") +
theme_bw() +
theme(axis.text.x = element_text(size=12, angle=90, vjust=0.5),
axis.text.y = element_text(size=12),
axis.title = element_text(size=12),
strip.text = element_text(size=12),
legend.text = element_text(size=12),
legend.title = element_text(size=12),
legend.position = "top") +
facet_wrap(~factor(gsub("12C-", "", substrate), levels=c("Xyl", "Ami", "Van", "Cel", "Pal")), nrow=1)
## Plot together
early_landuse.leg = g_legend(early_evenness_SOM.plot + theme(legend.direction = "vertical",
legend.box.background = element_rect(colour = "black")))
early_alpha_SOM.plot = cowplot::plot_grid(early_evenness_wilcox.plot + theme(legend.position = "none"),
early_evenness_SOM.plot + theme(legend.position = "none", axis.title.y = element_blank()),
early_landuse.leg,
early_evenness_DNA.plot + theme(legend.position = "none", axis.title.y = element_blank()),
ncol=2, labels=c("A", "B", "", "C"), rel_widths = c(0.5, 1, 0.5, 1))
early_alpha_SOM.plot
early_evenness_wilcox.df
## substrate Vstat pvalue padj sig
## V 12C-Xyl 177 1.310611e-01 2.098336e-01 NS
## V1 12C-Ami 70 2.300534e-04 5.751336e-04 ***
## V2 12C-Van 15 1.275912e-07 6.379560e-07 ***
## V3 12C-Cel 210 3.277192e-01 3.277192e-01 NS
## V4 12C-Pal 172 1.678669e-01 2.098336e-01 NS
early_SOM_evenness.df
## substrate r pvalue padj
## cor 12C-Xyl 0.4377734 0.015545185 0.07772593
## cor1 12C-Ami 0.4648663 0.009646017 0.04823009
## cor2 12C-Van 0.3574692 0.052456283 0.26228141
## cor3 12C-Cel 0.3947730 0.030854962 0.15427481
## cor4 12C-Pal 0.1486803 0.441452927 1.00000000
early_DNA_evenness.df
## substrate r pvalue padj
## cor 12C-Xyl 0.4805024 7.197024e-03 0.035985118
## cor1 12C-Ami 0.6554694 8.442419e-05 0.000422121
## cor2 12C-Van 0.5498244 1.647087e-03 0.008235434
## cor3 12C-Cel 0.3027813 1.038712e-01 0.519355835
## cor4 12C-Pal 0.1253467 5.170601e-01 1.000000000
Late timepoints are the second timepoint for each of the substrates (Day 4 for Xylose, amino acids, and vanillin; Day 28 for palmitic acid and cellulose). This is similar to the late timepoints for SIP and likely captures bacterial growth due more to secondary feeding and late feeding on substrates.
## Get bulk
bulk_meta.df = data.frame(sample_data(bulk.rare.physeq)) %>%
select(location, ecosystem, pH, organic_content_perc, DNA_conc__ng_ul) %>%
mutate(DNA_conc__ng_ul = as.numeric(as.character(DNA_conc__ng_ul)))
evenness_late_meta.df = alpha_div_treat.df %>%
filter(method == "evenness", time_point == "Late") %>%
tidyr::spread(key=treatment, value=diversity) %>%
mutate(delta_evenness = Treatment-Control) %>%
left_join(bulk_meta.df, by = c("ecosystem", "location")) %>%
mutate(ecosystem = factor(ecosystem, levels=c("agriculture", "meadow", "forest")))
## Compare to zero
late_evenness_wilcox.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
model = wilcox.test(x = filter(evenness_late_meta.df, substrate==sub)$delta_evenness, alternative = "less", mu=0)
model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
Vstat = model$statistic,
pvalue = model$p.value)
late_evenness_wilcox.df = rbind(late_evenness_wilcox.df, model.df)
}
late_evenness_wilcox.df = late_evenness_wilcox.df %>%
mutate(padj = p.adjust(pvalue, method = "BH", n = 5)) %>%
mutate(sig = ifelse(padj < 0.001, "***",
ifelse(padj < 0.01, "**",
ifelse(padj < 0.05, "*", "NS"))))
late_evenness_wilcox.plot = ggplot(data=evenness_late_meta.df, aes(x=substrate, y=delta_evenness)) +
geom_hline(yintercept = 0, linetype=2, color="red") +
geom_boxplot(outlier.shape = NA) +
geom_jitter(aes(color=ecosystem), alpha=0.5) +
geom_text(data=filter(late_evenness_wilcox.df, padj < 0.05), aes(x=substrate, label=sig), y=0.06, size=6) +
scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
labs(x="Substrate", y="Change in evenness", color="Land-use") +
lims(y=c(-0.3, 0.1)) +
theme_bw() +
theme(axis.text.x = element_text(size=12, angle=45, vjust=1, hjust=1),
axis.text.y = element_text(size=12),
axis.title = element_text(size=12),
strip.text = element_text(size=12),
legend.text = element_text(size=12),
legend.title = element_text(size=12),
legend.position = "top")
## Compare with organic content
late_SOM_evenness.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
sub.evenness.df = filter(evenness_late_meta.df, substrate==sub)
cor.res = cor.test(x=sub.evenness.df$organic_content_perc, y=sub.evenness.df$delta_evenness)
model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
r = cor.res$estimate,
pvalue = cor.res$p.value) %>%
mutate(padj = p.adjust(pvalue, method = "BH", n = 5))
late_SOM_evenness.df = rbind(late_SOM_evenness.df, model.df)
}
late_evenness_SOM.plot = ggplot(data=evenness_late_meta.df, aes(x=organic_content_perc, y=delta_evenness)) +
geom_smooth(method="lm", se=FALSE, color="grey70", size=0.75) +
geom_point(aes(color=ecosystem)) +
geom_text(data=late_SOM_evenness.df, aes(label=paste("r==", round(r, 3), sep="")),
x=0.03, y=-0.2, hjust=0, parse = TRUE) +
geom_text(data=late_SOM_evenness.df, aes(label=paste("p==", round(padj, 3), sep="")),
x=0.03, y=-0.235, hjust=0, parse = TRUE) +
scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
lims(y=c(-0.3, 0.1)) +
labs(x="% SOM in bulk soil", y="Change in evenness", color="Land-use") +
theme_bw() +
theme(axis.text.x = element_text(size=12, angle=90, vjust=0.5),
axis.text.y = element_text(size=12),
axis.title = element_text(size=12),
strip.text = element_text(size=12),
legend.text = element_text(size=12),
legend.title = element_text(size=12),
legend.position = "top") +
facet_wrap(~factor(gsub("12C-", "", substrate), levels=c("Xyl", "Ami", "Van", "Cel", "Pal")), nrow=1)
## Compare with DNA concentration
late_DNA_evenness.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
sub.evenness.df = filter(evenness_late_meta.df, substrate==sub)
cor.res = cor.test(x=sub.evenness.df$DNA_conc__ng_ul, y=sub.evenness.df$delta_evenness)
model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
r = cor.res$estimate,
pvalue = cor.res$p.value) %>%
mutate(padj = p.adjust(pvalue, method = "BH", n = 5))
late_DNA_evenness.df = rbind(late_DNA_evenness.df, model.df)
}
late_evenness_DNA.plot = ggplot(data=evenness_late_meta.df, aes(x=DNA_conc__ng_ul, y=delta_evenness)) +
geom_smooth(method="lm", se=FALSE, color="grey70", size=0.75) +
geom_point(aes(color=ecosystem)) +
geom_text(data=late_DNA_evenness.df, aes(label=paste("r==", round(r, 3), sep="")),
x=3, y=-0.2, hjust=0, parse = TRUE) +
geom_text(data=late_DNA_evenness.df, aes(label=paste("p==", round(padj, 3), sep="")),
x=3, y=-0.235, hjust=0, parse = TRUE) +
scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
lims(y=c(-0.3, 0.1)) +
labs(x="DNA Concentration", y="Change in evenness", color="Land-use") +
theme_bw() +
theme(axis.text.x = element_text(size=12, angle=90, vjust=0.5),
axis.text.y = element_text(size=12),
axis.title = element_text(size=12),
strip.text = element_text(size=12),
legend.text = element_text(size=12),
legend.title = element_text(size=12),
legend.position = "top") +
facet_wrap(~factor(gsub("12C-", "", substrate), levels=c("Xyl", "Ami", "Van", "Cel", "Pal")), nrow=1)
## Plot together
late_landuse.leg = g_legend(late_evenness_SOM.plot + theme(legend.direction = "vertical",
legend.box.background = element_rect(colour = "black")))
cowplot::plot_grid(late_evenness_wilcox.plot + theme(legend.position = "none"),
late_evenness_SOM.plot + theme(legend.position = "none", axis.title.y = element_blank()),
late_landuse.leg,
late_evenness_DNA.plot + theme(legend.position = "none", axis.title.y = element_blank()),
ncol=2, labels=c("A", "B", "", "C"), rel_widths = c(0.5, 1, 0.5, 1))
late_evenness_wilcox.df
## substrate Vstat pvalue padj sig
## V 12C-Xyl 215 3.651710e-01 6.086183e-01 NS
## V1 12C-Ami 98 2.332402e-03 5.831006e-03 **
## V2 12C-Van 18 2.356246e-07 1.178123e-06 ***
## V3 12C-Cel 230 6.090689e-01 7.526750e-01 NS
## V4 12C-Pal 249 7.526750e-01 7.526750e-01 NS
late_SOM_evenness.df
## substrate r pvalue padj
## cor 12C-Xyl 0.05516903 0.7721575116 1.000000000
## cor1 12C-Ami 0.41074059 0.0241539493 0.120769746
## cor2 12C-Van 0.58126223 0.0007557696 0.003778848
## cor3 12C-Cel -0.33922920 0.0718198662 0.359099331
## cor4 12C-Pal -0.05088678 0.7932062614 1.000000000
late_DNA_evenness.df
## substrate r pvalue padj
## cor 12C-Xyl 0.026575752 0.8891335818 1.000000000
## cor1 12C-Ami 0.383383545 0.0364996598 0.182498299
## cor2 12C-Van 0.585679112 0.0006731203 0.003365602
## cor3 12C-Cel -0.194922309 0.3109276590 1.000000000
## cor4 12C-Pal 0.005407977 0.9777882112 1.000000000
Since these are enrichments we cannot identify specific OTUs that have taken up a given substrate, however we can look at the community level change due to the substrate by comparing the treament microcosms to their corresponding water controls.
The first thing I need to do is measure the distance or dissimilarity between all microcosm communities. I will use three metrics for this: Bray-Curtis dissimilarity, unweighted UniFrac distance, and weighted UniFrac.
enr_BC.dist = vegdist(t(otu_table(enr.rare.physeq)), method="bray", binary=FALSE, diag=TRUE, upper=TRUE)
enr_uwUF.dist = distance(enr.rare.physeq, method="unifrac")
enr_wUF.dist = distance(enr.rare.physeq, method="wunifrac")
print("Bray-Curtis dissimilarity")
## [1] "Bray-Curtis dissimilarity"
enr_BC.adonis = adonis(formula = enr_BC.dist ~ ecosystem * substrate * day, data = as(sample_data(enr.rare.physeq), "data.frame"))
enr_BC.adonis
##
## Call:
## adonis(formula = enr_BC.dist ~ ecosystem * substrate * day, data = as(sample_data(enr.rare.physeq), "data.frame"))
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## ecosystem 2 11.411 5.7053 28.3341 0.12269 0.001 ***
## substrate 5 2.397 0.4794 2.3807 0.02577 0.001 ***
## day 1 0.357 0.3571 1.7734 0.00384 0.062 .
## ecosystem:substrate 10 1.156 0.1156 0.5741 0.01243 1.000
## ecosystem:day 2 0.185 0.0923 0.4583 0.00198 0.999
## substrate:day 5 0.303 0.0606 0.3010 0.00326 1.000
## ecosystem:substrate:day 10 0.475 0.0475 0.2361 0.00511 1.000
## Residuals 381 76.718 0.2014 0.82491
## Total 416 93.002 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("-----")
## [1] "-----"
print("Unweighted UniFrac distance")
## [1] "Unweighted UniFrac distance"
enr_uwUF.adonis = adonis(formula = enr_uwUF.dist ~ ecosystem * substrate * day, data = as(sample_data(enr.rare.physeq), "data.frame"))
enr_uwUF.adonis
##
## Call:
## adonis(formula = enr_uwUF.dist ~ ecosystem * substrate * day, data = as(sample_data(enr.rare.physeq), "data.frame"))
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## ecosystem 2 5.890 2.94479 15.8179 0.07255 0.001 ***
## substrate 5 1.071 0.21426 1.1509 0.01320 0.131
## day 1 0.276 0.27590 1.4820 0.00340 0.068 .
## ecosystem:substrate 10 1.155 0.11548 0.6203 0.01422 1.000
## ecosystem:day 2 0.241 0.12073 0.6485 0.00297 0.998
## substrate:day 5 0.575 0.11499 0.6177 0.00708 1.000
## ecosystem:substrate:day 10 1.046 0.10459 0.5618 0.01288 1.000
## Residuals 381 70.930 0.18617 0.87370
## Total 416 81.184 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("-----")
## [1] "-----"
print("Weighted UniFrac distance")
## [1] "Weighted UniFrac distance"
enr_wUF.adonis = adonis(formula = enr_wUF.dist ~ ecosystem * substrate * day, data = as(sample_data(enr.rare.physeq), "data.frame"))
enr_wUF.adonis
##
## Call:
## adonis(formula = enr_wUF.dist ~ ecosystem * substrate * day, data = as(sample_data(enr.rare.physeq), "data.frame"))
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## ecosystem 2 1.9896 0.99480 33.384 0.13575 0.001 ***
## substrate 5 0.8335 0.16670 5.594 0.05687 0.001 ***
## day 1 0.1283 0.12832 4.306 0.00876 0.002 **
## ecosystem:substrate 10 0.1916 0.01916 0.643 0.01307 0.992
## ecosystem:day 2 0.0312 0.01559 0.523 0.00213 0.930
## substrate:day 5 0.0649 0.01298 0.435 0.00443 1.000
## ecosystem:substrate:day 10 0.0638 0.00638 0.214 0.00436 1.000
## Residuals 381 11.3532 0.02980 0.77464
## Total 416 14.6561 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("-----")
## [1] "-----"
Substrate significantly explains varaition in community composition as does ecosystem. Now I want to see if the differences between water controls and treatments correlate with either SOM or [DNA].
bulk_meta.df = data.frame(sample_data(bulk.rare.physeq)) %>%
select(location, ecosystem, pH, organic_content_perc, DNA_conc__ng_ul) %>%
mutate(DNA_conc__ng_ul = as.numeric(as.character(DNA_conc__ng_ul)))
H2O.metadata = data.frame(sample_data(enr.rare.physeq)) %>%
mutate(X.Sample = as.character(X.Sample)) %>%
filter(substrate == "H2O-Con") %>%
rename(Control = X.Sample) %>%
select(Control, day, ecosystem, location)
treat.metadata = data.frame(sample_data(enr.rare.physeq)) %>%
mutate(X.Sample = as.character(X.Sample)) %>%
filter(substrate != "H2O-Con") %>%
rename(Treatment = X.Sample) %>%
select(Treatment, substrate, day, ecosystem, location)
paired.metadata = inner_join(H2O.metadata, treat.metadata, by = c("day", "ecosystem", "location"))
enr_BC.dist.df = as.matrix(enr_BC.dist)[paired.metadata$Treatment, paired.metadata$Control] %>%
as.data.frame %>%
tibble::rownames_to_column(var="X.Sample") %>%
rename(Treatment = X.Sample) %>%
tidyr::gather(key=Control, value=bray, -Treatment)
enr_uwUF.dist.df = as.matrix(enr_uwUF.dist)[paired.metadata$Treatment, paired.metadata$Control] %>%
as.data.frame %>%
tibble::rownames_to_column(var="X.Sample") %>%
rename(Treatment = X.Sample) %>%
tidyr::gather(key=Control, value=uwUF, -Treatment)
enr_wUF.dist.df = as.matrix(enr_wUF.dist)[paired.metadata$Treatment, paired.metadata$Control] %>%
as.data.frame %>%
tibble::rownames_to_column(var="X.Sample") %>%
rename(Treatment = X.Sample) %>%
tidyr::gather(key=Control, value=wUF, -Treatment)
enr_dist.df = full_join(enr_BC.dist.df, enr_uwUF.dist.df, by = c("Treatment", "Control")) %>%
full_join(enr_wUF.dist.df, by = c("Treatment", "Control")) %>%
inner_join(paired.metadata, by = c("Treatment", "Control")) %>%
mutate(time_point = ifelse(day %in% c(2, 14), "Early", "Late"),
ecosystem = factor(ecosystem, levels = c("agriculture", "meadow", "forest")),
substrate = factor(substrate, levels = c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal"))) %>%
left_join(bulk_meta.df, by = c("ecosystem", "location")) %>%
mutate(location = gsub("_", " ", location))
## Filter to just early timepoints
early_enr_dist.df = enr_dist.df %>%
filter(time_point == "Early") %>%
mutate(ecosystem = factor(ecosystem, levels=c("agriculture", "meadow", "forest")))
## Compare with organic content
early_SOM_wUF.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
sub.wUF.df = filter(early_enr_dist.df, substrate==sub)
cor.res = cor.test(x=sub.wUF.df$organic_content_perc, y=sub.wUF.df$wUF)
model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
r = cor.res$estimate,
pvalue = cor.res$p.value) %>%
mutate(padj = p.adjust(pvalue, method = "BH", n = 5))
early_SOM_wUF.df = rbind(early_SOM_wUF.df, model.df)
}
early_wUF_SOM.plot = ggplot(data=early_enr_dist.df, aes(x=organic_content_perc, y=wUF)) +
geom_smooth(method="lm", se=FALSE, color="grey70", size=0.75) +
geom_point(aes(color=ecosystem)) +
geom_text(data=early_SOM_wUF.df, aes(label=paste("r==", round(r, 3), sep="")),
x=0.04, y=0.4, hjust=0, parse = TRUE) +
geom_text(data=early_SOM_wUF.df, aes(label=paste("p==", round(padj, 3), sep="")),
x=0.04, y=0.36, hjust=0, parse = TRUE) +
scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
lims(y=c(0, 0.45)) +
labs(x="% SOM in bulk soil", y="Weighted UniFrac Distance", color="Land-use") +
theme_bw() +
theme(axis.text.x = element_text(size=12, angle=90, vjust=0.5),
axis.text.y = element_text(size=12),
axis.title = element_text(size=12),
strip.text = element_text(size=12),
legend.text = element_text(size=12),
legend.title = element_text(size=12),
legend.position = "top") +
facet_wrap(~factor(gsub("12C-", "", substrate), levels=c("Xyl", "Ami", "Van", "Cel", "Pal")), nrow=1)
## Compare with DNA concentration
early_DNA_wUF.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
sub.wUF.df = filter(early_enr_dist.df, substrate==sub)
cor.res = cor.test(x=sub.wUF.df$DNA_conc__ng_ul, y=sub.wUF.df$wUF)
model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
r = cor.res$estimate,
pvalue = cor.res$p.value) %>%
mutate(padj = p.adjust(pvalue, method = "BH", n = 5))
early_DNA_wUF.df = rbind(early_DNA_wUF.df, model.df)
}
early_wUF_DNA.plot = ggplot(data=early_enr_dist.df, aes(x=DNA_conc__ng_ul, y=wUF)) +
geom_smooth(method="lm", se=FALSE, color="grey70", size=0.75) +
geom_point(aes(color=ecosystem)) +
geom_text(data=early_DNA_wUF.df, aes(label=paste("r==", round(r, 3), sep="")),
x=5, y=0.4, hjust=0, parse = TRUE) +
geom_text(data=early_DNA_wUF.df, aes(label=paste("p==", round(padj, 3), sep="")),
x=5, y=0.36, hjust=0, parse = TRUE) +
scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
lims(y=c(0, 0.45)) +
labs(x="DNA Concentration", y="Weighted UniFrac Distance", color="Land-use") +
theme_bw() +
theme(axis.text.x = element_text(size=12, angle=90, vjust=0.5),
axis.text.y = element_text(size=12),
axis.title = element_text(size=12),
strip.text = element_text(size=12),
legend.text = element_text(size=12),
legend.title = element_text(size=12),
legend.position = "top") +
facet_wrap(~factor(gsub("12C-", "", substrate), levels=c("Xyl", "Ami", "Van", "Cel", "Pal")), nrow=1)
## Plot together
early_landuse.leg = g_legend(early_wUF_SOM.plot + theme(legend.direction = "horizontal",
legend.position = "top",
legend.margin=unit(c(0,0,0,0),"cm")))
cowplot::plot_grid(early_landuse.leg,
early_wUF_SOM.plot + theme(legend.position = "none"),
early_wUF_DNA.plot + theme(legend.position = "none"),
ncol=1, labels=c("", "A", "B"), rel_heights = c(0.1, 1, 1))
early_SOM_wUF.df
## substrate r pvalue padj
## cor 12C-Xyl -0.4394740 0.015102735 0.075513675
## cor1 12C-Ami -0.5635586 0.001183091 0.005915455
## cor2 12C-Van -0.5085936 0.004106756 0.020533781
## cor3 12C-Cel -0.3298842 0.075028616 0.375143082
## cor4 12C-Pal -0.3912330 0.035847250 0.179236250
early_DNA_wUF.df
## substrate r pvalue padj
## cor 12C-Xyl -0.3919137 0.0322006083 0.161003042
## cor1 12C-Ami -0.5861507 0.0006647827 0.003323913
## cor2 12C-Van -0.5794285 0.0007926151 0.003963076
## cor3 12C-Cel -0.1307706 0.4909543483 1.000000000
## cor4 12C-Pal -0.3894321 0.0367842529 0.183921265
## Filter to just late timepoints
late_enr_dist.df = enr_dist.df %>%
filter(time_point == "Late") %>%
mutate(ecosystem = factor(ecosystem, levels=c("agriculture", "meadow", "forest")))
## Compare with organic content
late_SOM_wUF.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
sub.wUF.df = filter(late_enr_dist.df, substrate==sub)
cor.res = cor.test(x=sub.wUF.df$organic_content_perc, y=sub.wUF.df$wUF)
model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
r = cor.res$estimate,
pvalue = cor.res$p.value) %>%
mutate(padj = p.adjust(pvalue, method = "BH", n = 5))
late_SOM_wUF.df = rbind(late_SOM_wUF.df, model.df)
}
late_wUF_SOM.plot = ggplot(data=late_enr_dist.df, aes(x=organic_content_perc, y=wUF)) +
geom_smooth(method="lm", se=FALSE, color="grey70", size=0.75) +
geom_point(aes(color=ecosystem)) +
geom_text(data=late_SOM_wUF.df, aes(label=paste("r==", round(r, 3), sep="")),
x=0.04, y=0.4, hjust=0, parse = TRUE) +
geom_text(data=late_SOM_wUF.df, aes(label=paste("p==", round(padj, 3), sep="")),
x=0.04, y=0.36, hjust=0, parse = TRUE) +
scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
lims(y=c(0, 0.45)) +
labs(x="% SOM in bulk soil", y="Weighted UniFrac Distance", color="Land-use") +
theme_bw() +
theme(axis.text.x = element_text(size=12, angle=90, vjust=0.5),
axis.text.y = element_text(size=12),
axis.title = element_text(size=12),
strip.text = element_text(size=12),
legend.text = element_text(size=12),
legend.title = element_text(size=12),
legend.position = "top") +
facet_wrap(~factor(gsub("12C-", "", substrate), levels=c("Xyl", "Ami", "Van", "Cel", "Pal")), nrow=1)
## Compare with DNA concentration
late_DNA_wUF.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
sub.wUF.df = filter(late_enr_dist.df, substrate==sub)
cor.res = cor.test(x=sub.wUF.df$DNA_conc__ng_ul, y=sub.wUF.df$wUF)
model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
r = cor.res$estimate,
pvalue = cor.res$p.value) %>%
mutate(padj = p.adjust(pvalue, method = "BH", n = 5))
late_DNA_wUF.df = rbind(late_DNA_wUF.df, model.df)
}
late_wUF_DNA.plot = ggplot(data=late_enr_dist.df, aes(x=DNA_conc__ng_ul, y=wUF)) +
geom_smooth(method="lm", se=FALSE, color="grey70", size=0.75) +
geom_point(aes(color=ecosystem)) +
geom_text(data=late_DNA_wUF.df, aes(label=paste("r==", round(r, 3), sep="")),
x=5, y=0.4, hjust=0, parse = TRUE) +
geom_text(data=late_DNA_wUF.df, aes(label=paste("p==", round(padj, 3), sep="")),
x=5, y=0.36, hjust=0, parse = TRUE) +
scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
lims(y=c(0, 0.45)) +
labs(x="DNA Concentration", y="Weighted UniFrac Distance", color="Land-use") +
theme_bw() +
theme(axis.text.x = element_text(size=12, angle=90, vjust=0.5),
axis.text.y = element_text(size=12),
axis.title = element_text(size=12),
strip.text = element_text(size=12),
legend.text = element_text(size=12),
legend.title = element_text(size=12),
legend.position = "top") +
facet_wrap(~factor(gsub("12C-", "", substrate), levels=c("Xyl", "Ami", "Van", "Cel", "Pal")), nrow=1)
## Plot together
late_landuse.leg = g_legend(late_wUF_SOM.plot + theme(legend.direction = "horizontal",
legend.position = "top",
legend.margin=unit(c(0,0,0,0),"cm")))
cowplot::plot_grid(late_landuse.leg,
late_wUF_SOM.plot + theme(legend.position = "none"),
late_wUF_DNA.plot + theme(legend.position = "none"),
ncol=1, labels=c("", "A", "B"), rel_heights = c(0.1, 1, 1))
late_SOM_wUF.df
## substrate r pvalue padj
## cor 12C-Xyl -0.5665699 0.0010981916 0.005490958
## cor1 12C-Ami -0.4295573 0.0178374274 0.089187137
## cor2 12C-Van -0.6256795 0.0002176509 0.001088255
## cor3 12C-Cel -0.4483317 0.0147202467 0.073601234
## cor4 12C-Pal -0.4150801 0.0251522068 0.125761034
late_DNA_wUF.df
## substrate r pvalue padj
## cor 12C-Xyl -0.5763353 0.0008583402 0.004291701
## cor1 12C-Ami -0.2342091 0.2128667039 1.000000000
## cor2 12C-Van -0.5619261 0.0012314691 0.006157345
## cor3 12C-Cel -0.4051031 0.0292561374 0.146280687
## cor4 12C-Pal -0.2615567 0.1705101106 0.852550553
These will be the plots from the analyses above that are used in the publication of this study (and my PhD dissertation).
early_landuse.leg = g_legend(early_evenness_SOM.plot + theme(legend.direction = "vertical",
legend.box.background = element_rect(colour = "black")))
early_DNAyield.plot = cowplot::plot_grid(early_evenness_wilcox.plot + theme(legend.position = "none"),
early_evenness_DNA.plot + theme(legend.position = "none") + labs(x="DNA yield from bulk soil (ng/µl)", y="Change in evenness"),
early_landuse.leg,
early_wUF_DNA.plot + theme(legend.position = "none") + labs(x="DNA yield from bulk soil (ng/µl)", y="Weighted UniFrac distance"),
ncol=2, labels=c("A", "B", "", "C"), rel_widths = c(0.5, 1, 0.5, 1))
early_DNAyield.plot
#ggsave(early_DNAyield.plot, filename = "/Users/sambarnett/Documents/Dissertation/figures/fig2_5.tiff",
# device = "tiff", width = 7, height = 6, units = "in")
For publication
carbon.conv = data.frame(substrate = c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal"),
carbon = factor(c("Xylose", "Amino acids", "Vanillin", "Cellulose", "Palmitic acid"),
levels=c("Xylose", "Amino acids", "Vanillin", "Cellulose", "Palmitic acid")))
pub_early_evenness_wilcox.plot = ggplot(data=left_join(evenness_early_meta.df, carbon.conv, by="substrate"), aes(x=carbon, y=delta_evenness)) +
geom_hline(yintercept = 0, linetype=2, color="red") +
geom_boxplot(outlier.shape = NA) +
geom_jitter(aes(color=ecosystem), alpha=0.5, size=1.5) +
geom_text(data=filter(left_join(early_evenness_wilcox.df, carbon.conv, by="substrate"), padj < 0.05), aes(x=carbon, label=sig), y=0.06, size=4) +
scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
labs(x="Substrate", y="Change in evenness", color="Land-use") +
lims(y=c(-0.3, 0.1)) +
theme_bw() +
theme(axis.text.x = element_text(size=6, angle=45, vjust=1, hjust=1),
axis.text.y = element_text(size=6),
axis.title = element_text(size=7),
axis.ticks = element_line(size=0.2),
strip.text = element_text(size=6),
legend.text = element_text(size=6),
legend.title = element_text(size=7),
legend.position = "right",
legend.box.background = element_rect(colour = "black"))
pub_early_evenness_DNA.plot = ggplot(data=left_join(evenness_early_meta.df, carbon.conv, by="substrate"), aes(x=DNA_conc__ng_ul, y=delta_evenness)) +
geom_smooth(method="lm", se=FALSE, color="black", size=0.75) +
geom_point(aes(color=ecosystem), size=1.5) +
geom_text(data=left_join(early_DNA_evenness.df, carbon.conv, by="substrate"), aes(label=paste("r==", round(r, 3), sep="")),
x=15, y=-0.2, hjust=0, parse = TRUE, size=(6*5/14)) +
geom_text(data=left_join(early_DNA_evenness.df, carbon.conv, by="substrate"), aes(label=paste("p==", round(padj, 3), sep="")),
x=15, y=-0.235, hjust=0, parse = TRUE, size=(6*5/14)) +
scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
scale_x_continuous(breaks=c(0, 50, 100)) +
lims(y=c(-0.3, 0.1)) +
labs(x="DNA Concentration", y="Change in evenness", color="Land-use") +
theme_bw() +
theme(axis.text.x = element_text(size=6, angle=90, vjust=0.5),
axis.text.y = element_text(size=6),
axis.title = element_text(size=7),
axis.ticks = element_line(size=0.2),
strip.text = element_text(size=6),
legend.text = element_text(size=6),
legend.title = element_text(size=7),
legend.position = "right") +
facet_wrap(~carbon, nrow=1)
pub_early_wUF_DNA.plot = ggplot(data=left_join(early_enr_dist.df, carbon.conv, by="substrate"), aes(x=DNA_conc__ng_ul, y=wUF)) +
geom_smooth(method="lm", se=FALSE, color="black", size=0.75) +
geom_point(aes(color=ecosystem), size=1.5) +
geom_text(data=left_join(early_DNA_wUF.df, carbon.conv, by="substrate"), aes(label=paste("r==", round(r, 3), sep="")),
x=15, y=0.4, hjust=0, parse = TRUE, size=(6*5/14)) +
geom_text(data=left_join(early_DNA_wUF.df, carbon.conv, by="substrate"), aes(label=paste("p==", round(padj, 3), sep="")),
x=15, y=0.36, hjust=0, parse = TRUE, size=(6*5/14)) +
scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
scale_x_continuous(breaks=c(0, 50, 100)) +
lims(y=c(0, 0.45)) +
labs(x="DNA Concentration", y="Weighted UniFrac Distance", color="Land-use") +
theme_bw() +
theme(axis.text.x = element_text(size=6, angle=90, vjust=0.5),
axis.text.y = element_text(size=6),
axis.title = element_text(size=7),
axis.ticks = element_line(size=0.2),
strip.text = element_text(size=6),
legend.text = element_text(size=6),
legend.title = element_text(size=7),
legend.position = "right") +
facet_wrap(~carbon, nrow=1)
pub_early_landuse.leg = g_legend(pub_early_wUF_DNA.plot + theme(legend.box.background = element_rect(colour = "black"),
legend.title = element_text(size=7, hjust=0.5)))
pub_early_evenness_wilcox_leg.plot = cowplot::plot_grid(pub_early_evenness_wilcox.plot + theme(legend.position = "none"),
pub_early_landuse.leg, ncol=2, rel_widths = c(1, 0.41))
pub_early_DNAyield.plot = cowplot::plot_grid(pub_early_evenness_wilcox_leg.plot,
pub_early_evenness_DNA.plot + theme(legend.position = "none") + labs(x="DNA yield from bulk soil (ng/µl)", y="Change in evenness"),
pub_early_wUF_DNA.plot + theme(legend.position = "none") + labs(x="DNA yield from bulk soil (ng/µl)", y="Weighted UniFrac distance"),
ncol=1, labels=c("a", "b", "c"), label_size = 10, rel_heights = c(0.9, 1, 1))
pub_early_DNAyield.plot
#ggsave(pub_early_DNAyield.plot, filename = "/Users/sambarnett/Documents/Buckley Lab/FullCyc2/manuscript/Figures/Fig3.tiff",
# device = "tiff", width = 3.46457, height = 7.08661, units = "in")
early_landuse.leg = g_legend(early_wUF_SOM.plot + theme(legend.direction = "horizontal",
legend.position = "top",
legend.margin=unit(c(0,0,0,0),"cm")))
early_SOM.plot = cowplot::plot_grid(early_landuse.leg,
early_evenness_SOM.plot + theme(legend.position = "none") + labs(x="Percent SOM in bulk soil (%)", y="Change in evenness"),
early_wUF_SOM.plot + theme(legend.position = "none") + labs(x="Percent SOM in bulk soil (%)", y="Weighted UniFrac distance"),
ncol=1, labels=c("", "A", "B"), rel_heights = c(0.1, 1, 1))
early_SOM.plot
#ggsave(early_SOM.plot, filename = "/Users/sambarnett/Documents/Dissertation/figures/figS2_11.tiff",
# device = "tiff", width = 5, height = 7, units = "in")
late_landuse.leg = g_legend(late_evenness_SOM.plot + theme(legend.direction = "vertical",
legend.box.background = element_rect(colour = "black")))
late_DNAyield.plot = cowplot::plot_grid(late_evenness_wilcox.plot + theme(legend.position = "none"),
late_evenness_DNA.plot + theme(legend.position = "none") + labs(x="DNA yield from bulk soil (ng/µl)", y="Change in evenness"),
late_landuse.leg,
late_wUF_DNA.plot + theme(legend.position = "none") + labs(x="DNA yield from bulk soil (ng/µl)", y="Weighted UniFrac distance"),
ncol=2, labels=c("A", "B", "", "C"), rel_widths = c(0.5, 1, 0.5, 1))
late_DNAyield.plot
#ggsave(late_DNAyield.plot, filename = "/Users/sambarnett/Documents/Dissertation/figures/figS2_12.tiff",
# device = "tiff", width = 7, height = 6, units = "in")
late_landuse.leg = g_legend(late_wUF_SOM.plot + theme(legend.direction = "horizontal",
legend.position = "top",
legend.margin=unit(c(0,0,0,0),"cm")))
late_SOM.plot = cowplot::plot_grid(late_landuse.leg,
late_evenness_SOM.plot + theme(legend.position = "none") + labs(x="Percent SOM in bulk soil (%)", y="Change in evenness"),
late_wUF_SOM.plot + theme(legend.position = "none") + labs(x="Percent SOM in bulk soil (%)", y="Weighted UniFrac distance"),
ncol=1, labels=c("", "A", "B"), rel_heights = c(0.1, 1, 1))
late_SOM.plot
#ggsave(late_SOM.plot, filename = "/Users/sambarnett/Documents/Dissertation/figures/figS2_13.tiff",
# device = "tiff", width = 5, height = 7, units = "in")
Now I want to see if I can identify any OTUs that are significantly enriched in the substrate treated samples compared to the control samples.
library(DESeq2)
library(knitr)
enr.deseq.df = data.frame()
for (eco in c("agriculture", "meadow", "forest")){
for (carbon in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
for (dia in unique(filter(data.frame(sample_data(enr.physeq)), ecosystem == eco, substrate == carbon)$day)){
sub.physeq = subset_samples(enr.physeq, ecosystem == eco & substrate %in% c(carbon, "H2O-Con") & day == dia)
sub.physeq = prune_taxa(taxa_sums(sub.physeq) > 0, sub.physeq)
sample_data(sub.physeq)$substrate = as.factor(sample_data(sub.physeq)$substrate)
sample_data(sub.physeq)$substrate = relevel(sample_data(sub.physeq)$substrate, "H2O-Con")
OTU.table = as.matrix(otu_table(sub.physeq))
OTU.table[OTU.table < 5] = 0
OTU.table[OTU.table >= 5] = 1
sparse = length(sample_names(sub.physeq)) * 0.25
sampleCount.df = data.frame(sample = rowSums(OTU.table)) %>%
tibble::rownames_to_column(var="OTU") %>%
filter(sample >= sparse)
sub.physeq = prune_taxa(sampleCount.df$OTU, sub.physeq)
sub.deseq = phyloseq_to_deseq2(sub.physeq, ~ location + substrate)
sub.deseq = DESeq(sub.deseq, betaPrior=TRUE)
sub.deseq.res = results(sub.deseq, lfcThreshold = .25,
contrast = c("substrate", carbon, "H2O-Con"),
altHypothesis = "greater",
test="Wald")
enr.deseq.df = rbind(enr.deseq.df, data.frame(sub.deseq.res) %>%
tibble::rownames_to_column(var="OTU") %>%
mutate(substrate = carbon, ecosystem = eco, day = dia))
}
}
}
#write.table(enr.deseq.df, file = "/Users/sambarnett/Documents/Buckley Lab/FullCyc2/enrichment_DESeq2_l2fc.txt", sep="\t", quote=FALSE, row.names = FALSE)
kable(enr.deseq.df %>%
filter(padj < 0.05) %>%
group_by(substrate, ecosystem, day) %>%
mutate(n_total_OTU = n()))
| OTU | baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | substrate | ecosystem | day | n_total_OTU |
|---|---|---|---|---|---|---|---|---|---|---|
| OTU.463 | 57.143056 | 1.7055406 | 0.3470587 | 4.193932 | 0.0000137 | 0.0118094 | 12C-Xyl | agriculture | 4 | 3 |
| OTU.66 | 108.855465 | 1.8827157 | 0.2496612 | 6.539725 | 0.0000000 | 0.0000001 | 12C-Xyl | agriculture | 4 | 3 |
| OTU.2 | 215.880615 | 1.1472726 | 0.2351432 | 3.815856 | 0.0000679 | 0.0389719 | 12C-Xyl | agriculture | 4 | 3 |
| OTU.9 | 823.875478 | 1.4954986 | 0.1899400 | 6.557326 | 0.0000000 | 0.0000000 | 12C-Xyl | agriculture | 2 | 10 |
| OTU.463 | 176.220818 | 2.3651105 | 0.3061382 | 6.909005 | 0.0000000 | 0.0000000 | 12C-Xyl | agriculture | 2 | 10 |
| OTU.1541 | 63.550455 | 1.1522105 | 0.2627946 | 3.433139 | 0.0002983 | 0.0098445 | 12C-Xyl | agriculture | 2 | 10 |
| OTU.951 | 84.136147 | 0.8679262 | 0.2059635 | 3.000173 | 0.0013491 | 0.0400692 | 12C-Xyl | agriculture | 2 | 10 |
| OTU.154 | 83.888332 | 1.5065962 | 0.2482874 | 5.061054 | 0.0000002 | 0.0000155 | 12C-Xyl | agriculture | 2 | 10 |
| OTU.315 | 477.770963 | 1.5345565 | 0.3461871 | 3.710585 | 0.0001034 | 0.0051178 | 12C-Xyl | agriculture | 2 | 10 |
| OTU.8 | 1616.901682 | 1.2408664 | 0.2885439 | 3.434023 | 0.0002973 | 0.0098445 | 12C-Xyl | agriculture | 2 | 10 |
| OTU.66 | 124.839100 | 1.9549236 | 0.3147849 | 5.416154 | 0.0000000 | 0.0000030 | 12C-Xyl | agriculture | 2 | 10 |
| OTU.2 | 249.987495 | 1.3658821 | 0.3072398 | 3.631958 | 0.0001406 | 0.0059671 | 12C-Xyl | agriculture | 2 | 10 |
| OTU.187 | 64.905291 | 1.6165815 | 0.2975099 | 4.593398 | 0.0000022 | 0.0001295 | 12C-Xyl | agriculture | 2 | 10 |
| OTU.9 | 1402.221020 | 2.2974544 | 0.2917992 | 7.016655 | 0.0000000 | 0.0000000 | 12C-Ami | agriculture | 4 | 14 |
| OTU.463 | 148.253559 | 2.4180415 | 0.3429677 | 6.321416 | 0.0000000 | 0.0000000 | 12C-Ami | agriculture | 4 | 14 |
| OTU.3782 | 12.433210 | 1.9041180 | 0.3626178 | 4.561601 | 0.0000025 | 0.0002191 | 12C-Ami | agriculture | 4 | 14 |
| OTU.315 | 392.139985 | 1.9600863 | 0.2492455 | 6.861052 | 0.0000000 | 0.0000000 | 12C-Ami | agriculture | 4 | 14 |
| OTU.8 | 1100.658842 | 1.6892232 | 0.2190543 | 6.570168 | 0.0000000 | 0.0000000 | 12C-Ami | agriculture | 4 | 14 |
| OTU.2396 | 27.086295 | 1.8923076 | 0.3811749 | 4.308541 | 0.0000082 | 0.0006384 | 12C-Ami | agriculture | 4 | 14 |
| OTU.23 | 93.608292 | 1.5439977 | 0.2326064 | 5.563036 | 0.0000000 | 0.0000015 | 12C-Ami | agriculture | 4 | 14 |
| OTU.1386 | 58.258922 | 1.2905180 | 0.2966817 | 3.507186 | 0.0002264 | 0.0125672 | 12C-Ami | agriculture | 4 | 14 |
| OTU.4461 | 24.743522 | 1.5908981 | 0.3615954 | 3.708283 | 0.0001043 | 0.0067557 | 12C-Ami | agriculture | 4 | 14 |
| OTU.7776 | 115.221961 | 0.8563077 | 0.1714353 | 3.536657 | 0.0002026 | 0.0121100 | 12C-Ami | agriculture | 4 | 14 |
| OTU.66 | 179.139721 | 2.6579874 | 0.2494062 | 9.654884 | 0.0000000 | 0.0000000 | 12C-Ami | agriculture | 4 | 14 |
| OTU.2 | 460.160815 | 1.6191671 | 0.3311080 | 4.135108 | 0.0000177 | 0.0012530 | 12C-Ami | agriculture | 4 | 14 |
| OTU.16149 | 21.593242 | 3.4617346 | 0.4060604 | 7.909500 | 0.0000000 | 0.0000000 | 12C-Ami | agriculture | 4 | 14 |
| OTU.3 | 31.418578 | 2.3544258 | 0.4180531 | 5.033872 | 0.0000002 | 0.0000233 | 12C-Ami | agriculture | 4 | 14 |
| OTU.3589 | 14.800817 | 1.5392306 | 0.4049181 | 3.183929 | 0.0007265 | 0.0303748 | 12C-Ami | agriculture | 2 | 17 |
| OTU.9 | 2031.842266 | 2.6071517 | 0.2382931 | 9.891816 | 0.0000000 | 0.0000000 | 12C-Ami | agriculture | 2 | 17 |
| OTU.463 | 245.424035 | 2.6656187 | 0.3248678 | 7.435699 | 0.0000000 | 0.0000000 | 12C-Ami | agriculture | 2 | 17 |
| OTU.3782 | 14.552128 | 2.0661450 | 0.3948885 | 4.599134 | 0.0000021 | 0.0001774 | 12C-Ami | agriculture | 2 | 17 |
| OTU.315 | 535.845657 | 2.2230735 | 0.2927950 | 6.738753 | 0.0000000 | 0.0000000 | 12C-Ami | agriculture | 2 | 17 |
| OTU.8 | 1912.137734 | 1.8269501 | 0.2647118 | 5.957233 | 0.0000000 | 0.0000001 | 12C-Ami | agriculture | 2 | 17 |
| OTU.2396 | 43.956143 | 1.8111898 | 0.4107461 | 3.800863 | 0.0000721 | 0.0043848 | 12C-Ami | agriculture | 2 | 17 |
| OTU.2513 | 62.213661 | 1.1788555 | 0.2616336 | 3.550216 | 0.0001925 | 0.0091967 | 12C-Ami | agriculture | 2 | 17 |
| OTU.1386 | 66.543042 | 2.1527448 | 0.2855800 | 6.662739 | 0.0000000 | 0.0000000 | 12C-Ami | agriculture | 2 | 17 |
| OTU.4461 | 32.142982 | 1.6209049 | 0.3337316 | 4.107807 | 0.0000200 | 0.0013361 | 12C-Ami | agriculture | 2 | 17 |
| OTU.193 | 72.464175 | 2.3130630 | 0.4535396 | 4.548804 | 0.0000027 | 0.0002005 | 12C-Ami | agriculture | 2 | 17 |
| OTU.20 | 757.867525 | 0.7834658 | 0.1667190 | 3.199790 | 0.0006876 | 0.0303748 | 12C-Ami | agriculture | 2 | 17 |
| OTU.66 | 210.384740 | 2.3425108 | 0.3797888 | 5.509670 | 0.0000000 | 0.0000017 | 12C-Ami | agriculture | 2 | 17 |
| OTU.2 | 546.506627 | 1.6335341 | 0.3685922 | 3.753563 | 0.0000872 | 0.0048597 | 12C-Ami | agriculture | 2 | 17 |
| OTU.3 | 113.241648 | 2.8100466 | 0.4301395 | 5.951666 | 0.0000000 | 0.0000001 | 12C-Ami | agriculture | 2 | 17 |
| OTU.711 | 32.047650 | 1.3359246 | 0.3043876 | 3.567572 | 0.0001802 | 0.0091967 | 12C-Ami | agriculture | 2 | 17 |
| OTU.187 | 53.188007 | 1.0761760 | 0.2623858 | 3.148707 | 0.0008200 | 0.0322683 | 12C-Ami | agriculture | 2 | 17 |
| OTU.1 | 980.734255 | 2.6471368 | 0.5128590 | 4.674066 | 0.0000015 | 0.0013451 | 12C-Van | agriculture | 2 | 4 |
| OTU.66 | 211.157488 | 2.0560193 | 0.4797711 | 3.764335 | 0.0000835 | 0.0380326 | 12C-Van | agriculture | 2 | 4 |
| OTU.2 | 2180.028131 | 2.1732379 | 0.4791039 | 4.014240 | 0.0000298 | 0.0181100 | 12C-Van | agriculture | 2 | 4 |
| OTU.3 | 1596.345820 | 3.3653851 | 0.4892176 | 6.368097 | 0.0000000 | 0.0000002 | 12C-Van | agriculture | 2 | 4 |
| OTU.8 | 1850.903401 | 2.3176915 | 0.3966413 | 5.213001 | 0.0000001 | 0.0000388 | 12C-Van | agriculture | 4 | 6 |
| OTU.2396 | 90.659803 | 3.0985649 | 0.4914833 | 5.795852 | 0.0000000 | 0.0000028 | 12C-Van | agriculture | 4 | 6 |
| OTU.1 | 589.750952 | 3.0252483 | 0.5020376 | 5.527970 | 0.0000000 | 0.0000090 | 12C-Van | agriculture | 4 | 6 |
| OTU.66 | 167.914591 | 2.7058574 | 0.3884788 | 6.321728 | 0.0000000 | 0.0000002 | 12C-Van | agriculture | 4 | 6 |
| OTU.2 | 1069.948292 | 2.3885562 | 0.4823726 | 4.433412 | 0.0000046 | 0.0012908 | 12C-Van | agriculture | 4 | 6 |
| OTU.56 | 193.226833 | 2.0932142 | 0.3779803 | 4.876482 | 0.0000005 | 0.0001804 | 12C-Van | agriculture | 4 | 6 |
| OTU.18 | 83.450918 | 1.4174655 | 0.2508977 | 4.653154 | 0.0000016 | 0.0032068 | 12C-Cel | agriculture | 14 | 1 |
| OTU.131 | 43.772174 | 1.5132675 | 0.3099683 | 4.075473 | 0.0000230 | 0.0165366 | 12C-Pal | agriculture | 28 | 3 |
| OTU.18 | 42.941360 | 1.4081744 | 0.2860221 | 4.049247 | 0.0000257 | 0.0165366 | 12C-Pal | agriculture | 28 | 3 |
| OTU.4420 | 113.862921 | 2.3114293 | 0.3187109 | 6.468023 | 0.0000000 | 0.0000001 | 12C-Pal | agriculture | 28 | 3 |
| OTU.9 | 307.578454 | 1.5465536 | 0.2465028 | 5.259791 | 0.0000001 | 0.0000171 | 12C-Xyl | meadow | 2 | 7 |
| OTU.951 | 23.722085 | 1.4350722 | 0.2565998 | 4.618367 | 0.0000019 | 0.0003055 | 12C-Xyl | meadow | 2 | 7 |
| OTU.154 | 110.673703 | 1.4643399 | 0.2625814 | 4.624622 | 0.0000019 | 0.0003055 | 12C-Xyl | meadow | 2 | 7 |
| OTU.2396 | 19.764453 | 1.6472424 | 0.3547402 | 3.938777 | 0.0000409 | 0.0055457 | 12C-Xyl | meadow | 2 | 7 |
| OTU.66 | 28.053223 | 2.0078524 | 0.2912671 | 6.035190 | 0.0000000 | 0.0000003 | 12C-Xyl | meadow | 2 | 7 |
| OTU.2 | 180.311952 | 1.7821720 | 0.2239602 | 6.841270 | 0.0000000 | 0.0000000 | 12C-Xyl | meadow | 2 | 7 |
| OTU.187 | 51.968729 | 1.6904341 | 0.2269908 | 6.345781 | 0.0000000 | 0.0000001 | 12C-Xyl | meadow | 2 | 7 |
| OTU.2 | 291.607731 | 1.5134239 | 0.2343700 | 5.390724 | 0.0000000 | 0.0000269 | 12C-Ami | meadow | 4 | 2 |
| OTU.3 | 67.375845 | 2.3313231 | 0.3385215 | 6.148274 | 0.0000000 | 0.0000006 | 12C-Ami | meadow | 4 | 2 |
| OTU.9 | 390.716931 | 1.9120133 | 0.2430766 | 6.837407 | 0.0000000 | 0.0000000 | 12C-Ami | meadow | 2 | 8 |
| OTU.3782 | 6.986803 | 1.5342235 | 0.3496703 | 3.672670 | 0.0001200 | 0.0213026 | 12C-Ami | meadow | 2 | 8 |
| OTU.1863 | 63.905406 | 1.7674692 | 0.3457300 | 4.389175 | 0.0000057 | 0.0013464 | 12C-Ami | meadow | 2 | 8 |
| OTU.315 | 168.680186 | 1.4360614 | 0.2688758 | 4.411186 | 0.0000051 | 0.0013464 | 12C-Ami | meadow | 2 | 8 |
| OTU.66 | 27.246631 | 1.6959146 | 0.3149271 | 4.591267 | 0.0000022 | 0.0007820 | 12C-Ami | meadow | 2 | 8 |
| OTU.2 | 256.841475 | 1.8312129 | 0.2188198 | 7.226096 | 0.0000000 | 0.0000000 | 12C-Ami | meadow | 2 | 8 |
| OTU.16149 | 9.175487 | 1.7383960 | 0.3551714 | 4.190641 | 0.0000139 | 0.0028214 | 12C-Ami | meadow | 2 | 8 |
| OTU.3 | 41.985175 | 2.6785968 | 0.3498060 | 6.942696 | 0.0000000 | 0.0000000 | 12C-Ami | meadow | 2 | 8 |
| OTU.3782 | 18.765526 | 2.3314754 | 0.3499449 | 5.948010 | 0.0000000 | 0.0000010 | 12C-Van | meadow | 4 | 5 |
| OTU.66 | 45.834871 | 1.7014041 | 0.3599458 | 4.032285 | 0.0000276 | 0.0104398 | 12C-Van | meadow | 4 | 5 |
| OTU.2 | 828.031945 | 2.7981725 | 0.2536109 | 10.047569 | 0.0000000 | 0.0000000 | 12C-Van | meadow | 4 | 5 |
| OTU.3 | 367.006897 | 1.8751572 | 0.3784055 | 4.294750 | 0.0000087 | 0.0044072 | 12C-Van | meadow | 4 | 5 |
| OTU.29 | 23.315455 | 1.6340944 | 0.3734184 | 3.706551 | 0.0001051 | 0.0317673 | 12C-Van | meadow | 4 | 5 |
| OTU.85 | 50.025534 | 1.4205890 | 0.3140419 | 3.727494 | 0.0000967 | 0.0070973 | 12C-Van | meadow | 2 | 12 |
| OTU.35 | 83.736607 | 1.1173289 | 0.2567176 | 3.378533 | 0.0003644 | 0.0202832 | 12C-Van | meadow | 2 | 12 |
| OTU.2977 | 43.992439 | 1.4438190 | 0.3223333 | 3.703679 | 0.0001062 | 0.0070973 | 12C-Van | meadow | 2 | 12 |
| OTU.98 | 26.822922 | 1.5949465 | 0.3292538 | 4.084832 | 0.0000221 | 0.0019933 | 12C-Van | meadow | 2 | 12 |
| OTU.3782 | 20.076912 | 3.0564123 | 0.3232447 | 8.682006 | 0.0000000 | 0.0000000 | 12C-Van | meadow | 2 | 12 |
| OTU.1166 | 16.420254 | 1.6638561 | 0.2746476 | 5.147891 | 0.0000001 | 0.0000147 | 12C-Van | meadow | 2 | 12 |
| OTU.2396 | 50.033273 | 2.6333978 | 0.3383678 | 7.043808 | 0.0000000 | 0.0000000 | 12C-Van | meadow | 2 | 12 |
| OTU.66 | 46.853527 | 2.5619807 | 0.3194617 | 7.237114 | 0.0000000 | 0.0000000 | 12C-Van | meadow | 2 | 12 |
| OTU.2 | 818.088037 | 2.2775018 | 0.3727713 | 5.438996 | 0.0000000 | 0.0000036 | 12C-Van | meadow | 2 | 12 |
| OTU.16149 | 12.255583 | 1.6074319 | 0.4011831 | 3.383572 | 0.0003577 | 0.0202832 | 12C-Van | meadow | 2 | 12 |
| OTU.3 | 598.496711 | 3.2789701 | 0.4011677 | 7.550383 | 0.0000000 | 0.0000000 | 12C-Van | meadow | 2 | 12 |
| OTU.223 | 42.460611 | 1.9160213 | 0.4097038 | 4.066405 | 0.0000239 | 0.0019933 | 12C-Van | meadow | 2 | 12 |
| OTU.1 | 354.827089 | 0.9391310 | 0.1456613 | 4.731050 | 0.0000011 | 0.0016920 | 12C-Xyl | forest | 4 | 2 |
| OTU.2 | 2283.299024 | 1.2753339 | 0.2374383 | 4.318317 | 0.0000079 | 0.0059549 | 12C-Xyl | forest | 4 | 2 |
| OTU.9 | 279.245634 | 1.2881788 | 0.1957360 | 5.303976 | 0.0000001 | 0.0000441 | 12C-Ami | forest | 2 | 2 |
| OTU.3 | 161.998275 | 2.7876475 | 0.3552902 | 7.142463 | 0.0000000 | 0.0000000 | 12C-Ami | forest | 2 | 2 |
| OTU.2 | 2911.058579 | 1.0187588 | 0.1898262 | 4.049804 | 0.0000256 | 0.0408034 | 12C-Ami | forest | 4 | 1 |
| OTU.66 | 64.385275 | 1.5741686 | 0.2865746 | 4.620677 | 0.0000019 | 0.0026750 | 12C-Van | forest | 2 | 2 |
| OTU.2 | 3957.520597 | 1.7175488 | 0.3265153 | 4.494579 | 0.0000035 | 0.0026750 | 12C-Van | forest | 2 | 2 |
| OTU.2 | 4835.350981 | 1.4233702 | 0.2440426 | 4.808054 | 0.0000008 | 0.0012177 | 12C-Van | forest | 4 | 1 |
enr.deseq.tax.df = data.frame(tax_table(enr.physeq), stringsAsFactors = FALSE) %>%
tibble::rownames_to_column(var="OTU") %>%
right_join(enr.deseq.df, by="OTU")
enr.deseq.tax.sum = enr.deseq.tax.df %>%
filter(padj < 0.05) %>%
mutate(taxa = ifelse(Phylum == "Proteobacteria", Class,
ifelse(is.na(Phylum), "Unclassified", Phylum)),
period = ifelse(day %in% c(2, 14), "Early", "Late")) %>%
group_by(ecosystem, substrate, period, taxa) %>%
summarize(n_OTU = n()) %>%
as.data.frame
ggplot(data = enr.deseq.tax.sum, aes(x=ecosystem, y=n_OTU, fill=taxa)) +
geom_bar(stat = "identity") +
facet_grid(substrate~period)
Are any of these substrate enriched OTUs also 13C-labeled in the SIP study?
## Save results for publication table
enr.tax.df = data.frame(tax_table(enr.physeq), stringsAsFactors = FALSE) %>%
tibble::rownames_to_column(var="OTU") %>%
filter(OTU %in% filter(enr.deseq.df, padj < 0.05)$OTU)
incorp.enr.df = l2fc.df %>%
filter(padj < 0.05) %>%
select(OTU, substrate, ecosystem) %>%
unique %>%
mutate(substrate = gsub("13C", "12C", substrate),
SIP_labeled = "Yes") %>%
inner_join(enr.deseq.df %>% filter(padj < 0.05),
by = c("OTU", "substrate", "ecosystem")) %>%
select(OTU, substrate, ecosystem, SIP_labeled) %>%
right_join(enr.deseq.df %>% filter(padj < 0.05),
by = c("OTU", "substrate", "ecosystem")) %>%
mutate(SIP_labeled = ifelse(is.na(SIP_labeled), "No", SIP_labeled)) %>%
left_join(enr.tax.df, by="OTU")
#write.table(incorp.enr.df, file = "/Users/sambarnett/Desktop/labeled_enrichment_DESeq2_l2fc.txt", sep="\t", quote=FALSE, row.names = FALSE)
kable(incorp.enr.df)
| OTU | substrate | ecosystem | SIP_labeled | baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | day | Domain | Phylum | Class | Order | Family | Genus | Species |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OTU.66 | 12C-Ami | meadow | Yes | 27.246631 | 1.6959146 | 0.3149271 | 4.591267 | 0.0000022 | 0.0007820 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.9 | 12C-Ami | meadow | Yes | 390.716931 | 1.9120133 | 0.2430766 | 6.837407 | 0.0000000 | 0.0000000 | 2 | Bacteria | Actinobacteria | Actinobacteria | Micrococcales | Micrococcaceae | Ambiguous_taxa | Ambiguous_taxa |
| OTU.3 | 12C-Ami | meadow | Yes | 67.375845 | 2.3313231 | 0.3385215 | 6.148274 | 0.0000000 | 0.0000006 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Pseudomonadales | Pseudomonadaceae | Pseudomonas | Ambiguous_taxa |
| OTU.3 | 12C-Ami | meadow | Yes | 41.985175 | 2.6785968 | 0.3498060 | 6.942696 | 0.0000000 | 0.0000000 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Pseudomonadales | Pseudomonadaceae | Pseudomonas | Ambiguous_taxa |
| OTU.3 | 12C-Ami | meadow | Yes | 67.375845 | 2.3313231 | 0.3385215 | 6.148274 | 0.0000000 | 0.0000006 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Pseudomonadales | Pseudomonadaceae | Pseudomonas | Ambiguous_taxa |
| OTU.3 | 12C-Ami | meadow | Yes | 41.985175 | 2.6785968 | 0.3498060 | 6.942696 | 0.0000000 | 0.0000000 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Pseudomonadales | Pseudomonadaceae | Pseudomonas | Ambiguous_taxa |
| OTU.2 | 12C-Ami | meadow | Yes | 291.607731 | 1.5134239 | 0.2343700 | 5.390724 | 0.0000000 | 0.0000269 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.2 | 12C-Ami | meadow | Yes | 256.841475 | 1.8312129 | 0.2188198 | 7.226096 | 0.0000000 | 0.0000000 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.2 | 12C-Ami | meadow | Yes | 291.607731 | 1.5134239 | 0.2343700 | 5.390724 | 0.0000000 | 0.0000269 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.2 | 12C-Ami | meadow | Yes | 256.841475 | 1.8312129 | 0.2188198 | 7.226096 | 0.0000000 | 0.0000000 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.3782 | 12C-Ami | meadow | Yes | 6.986803 | 1.5342235 | 0.3496703 | 3.672670 | 0.0001200 | 0.0213026 | 2 | Bacteria | Actinobacteria | Actinobacteria | Corynebacteriales | Nocardiaceae | Rhodococcus | NA |
| OTU.16149 | 12C-Ami | meadow | Yes | 9.175487 | 1.7383960 | 0.3551714 | 4.190641 | 0.0000139 | 0.0028214 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Pseudomonadales | Pseudomonadaceae | Pseudomonas | Pseudomonas koreensis |
| OTU.2 | 12C-Xyl | agriculture | Yes | 215.880615 | 1.1472726 | 0.2351432 | 3.815856 | 0.0000679 | 0.0389719 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.2 | 12C-Xyl | agriculture | Yes | 249.987495 | 1.3658821 | 0.3072398 | 3.631958 | 0.0001406 | 0.0059671 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.2 | 12C-Xyl | agriculture | Yes | 215.880615 | 1.1472726 | 0.2351432 | 3.815856 | 0.0000679 | 0.0389719 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.2 | 12C-Xyl | agriculture | Yes | 249.987495 | 1.3658821 | 0.3072398 | 3.631958 | 0.0001406 | 0.0059671 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.66 | 12C-Xyl | agriculture | Yes | 108.855465 | 1.8827157 | 0.2496612 | 6.539725 | 0.0000000 | 0.0000001 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.66 | 12C-Xyl | agriculture | Yes | 124.839100 | 1.9549236 | 0.3147849 | 5.416154 | 0.0000000 | 0.0000030 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.66 | 12C-Xyl | agriculture | Yes | 108.855465 | 1.8827157 | 0.2496612 | 6.539725 | 0.0000000 | 0.0000001 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.66 | 12C-Xyl | agriculture | Yes | 124.839100 | 1.9549236 | 0.3147849 | 5.416154 | 0.0000000 | 0.0000030 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.3 | 12C-Van | meadow | Yes | 367.006897 | 1.8751572 | 0.3784055 | 4.294750 | 0.0000087 | 0.0044072 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Pseudomonadales | Pseudomonadaceae | Pseudomonas | Ambiguous_taxa |
| OTU.3 | 12C-Van | meadow | Yes | 598.496711 | 3.2789701 | 0.4011677 | 7.550383 | 0.0000000 | 0.0000000 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Pseudomonadales | Pseudomonadaceae | Pseudomonas | Ambiguous_taxa |
| OTU.3 | 12C-Van | meadow | Yes | 367.006897 | 1.8751572 | 0.3784055 | 4.294750 | 0.0000087 | 0.0044072 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Pseudomonadales | Pseudomonadaceae | Pseudomonas | Ambiguous_taxa |
| OTU.3 | 12C-Van | meadow | Yes | 598.496711 | 3.2789701 | 0.4011677 | 7.550383 | 0.0000000 | 0.0000000 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Pseudomonadales | Pseudomonadaceae | Pseudomonas | Ambiguous_taxa |
| OTU.3782 | 12C-Van | meadow | Yes | 18.765526 | 2.3314754 | 0.3499449 | 5.948010 | 0.0000000 | 0.0000010 | 4 | Bacteria | Actinobacteria | Actinobacteria | Corynebacteriales | Nocardiaceae | Rhodococcus | NA |
| OTU.3782 | 12C-Van | meadow | Yes | 20.076912 | 3.0564123 | 0.3232447 | 8.682006 | 0.0000000 | 0.0000000 | 2 | Bacteria | Actinobacteria | Actinobacteria | Corynebacteriales | Nocardiaceae | Rhodococcus | NA |
| OTU.3782 | 12C-Van | meadow | Yes | 18.765526 | 2.3314754 | 0.3499449 | 5.948010 | 0.0000000 | 0.0000010 | 4 | Bacteria | Actinobacteria | Actinobacteria | Corynebacteriales | Nocardiaceae | Rhodococcus | NA |
| OTU.3782 | 12C-Van | meadow | Yes | 20.076912 | 3.0564123 | 0.3232447 | 8.682006 | 0.0000000 | 0.0000000 | 2 | Bacteria | Actinobacteria | Actinobacteria | Corynebacteriales | Nocardiaceae | Rhodococcus | NA |
| OTU.1166 | 12C-Van | meadow | Yes | 16.420254 | 1.6638561 | 0.2746476 | 5.147891 | 0.0000001 | 0.0000147 | 2 | Bacteria | Actinobacteria | Actinobacteria | Corynebacteriales | Nocardiaceae | Rhodococcus | Ambiguous_taxa |
| OTU.2 | 12C-Ami | agriculture | Yes | 460.160815 | 1.6191671 | 0.3311080 | 4.135108 | 0.0000177 | 0.0012530 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.2 | 12C-Ami | agriculture | Yes | 546.506627 | 1.6335341 | 0.3685922 | 3.753563 | 0.0000872 | 0.0048597 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.2 | 12C-Ami | agriculture | Yes | 460.160815 | 1.6191671 | 0.3311080 | 4.135108 | 0.0000177 | 0.0012530 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.2 | 12C-Ami | agriculture | Yes | 546.506627 | 1.6335341 | 0.3685922 | 3.753563 | 0.0000872 | 0.0048597 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.66 | 12C-Ami | agriculture | Yes | 179.139721 | 2.6579874 | 0.2494062 | 9.654884 | 0.0000000 | 0.0000000 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.66 | 12C-Ami | agriculture | Yes | 210.384740 | 2.3425108 | 0.3797888 | 5.509670 | 0.0000000 | 0.0000017 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.66 | 12C-Ami | agriculture | Yes | 179.139721 | 2.6579874 | 0.2494062 | 9.654884 | 0.0000000 | 0.0000000 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.66 | 12C-Ami | agriculture | Yes | 210.384740 | 2.3425108 | 0.3797888 | 5.509670 | 0.0000000 | 0.0000017 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.193 | 12C-Ami | agriculture | Yes | 72.464175 | 2.3130630 | 0.4535396 | 4.548804 | 0.0000027 | 0.0002005 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Cupriavidus | Ambiguous_taxa |
| OTU.16149 | 12C-Ami | agriculture | Yes | 21.593242 | 3.4617346 | 0.4060604 | 7.909500 | 0.0000000 | 0.0000000 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Pseudomonadales | Pseudomonadaceae | Pseudomonas | Pseudomonas koreensis |
| OTU.3782 | 12C-Ami | agriculture | Yes | 12.433210 | 1.9041180 | 0.3626178 | 4.561601 | 0.0000025 | 0.0002191 | 4 | Bacteria | Actinobacteria | Actinobacteria | Corynebacteriales | Nocardiaceae | Rhodococcus | NA |
| OTU.3782 | 12C-Ami | agriculture | Yes | 14.552128 | 2.0661450 | 0.3948885 | 4.599134 | 0.0000021 | 0.0001774 | 2 | Bacteria | Actinobacteria | Actinobacteria | Corynebacteriales | Nocardiaceae | Rhodococcus | NA |
| OTU.3782 | 12C-Ami | agriculture | Yes | 12.433210 | 1.9041180 | 0.3626178 | 4.561601 | 0.0000025 | 0.0002191 | 4 | Bacteria | Actinobacteria | Actinobacteria | Corynebacteriales | Nocardiaceae | Rhodococcus | NA |
| OTU.3782 | 12C-Ami | agriculture | Yes | 14.552128 | 2.0661450 | 0.3948885 | 4.599134 | 0.0000021 | 0.0001774 | 2 | Bacteria | Actinobacteria | Actinobacteria | Corynebacteriales | Nocardiaceae | Rhodococcus | NA |
| OTU.223 | 12C-Van | meadow | Yes | 42.460611 | 1.9160213 | 0.4097038 | 4.066405 | 0.0000239 | 0.0019933 | 2 | Bacteria | Proteobacteria | Alphaproteobacteria | Sphingomonadales | Sphingomonadaceae | Novosphingobium | uncultured bacterium |
| OTU.3 | 12C-Ami | forest | Yes | 161.998275 | 2.7876475 | 0.3552902 | 7.142463 | 0.0000000 | 0.0000000 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Pseudomonadales | Pseudomonadaceae | Pseudomonas | Ambiguous_taxa |
| OTU.2 | 12C-Ami | forest | Yes | 2911.058579 | 1.0187588 | 0.1898262 | 4.049804 | 0.0000256 | 0.0408034 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.9 | 12C-Ami | agriculture | Yes | 1402.221020 | 2.2974544 | 0.2917992 | 7.016655 | 0.0000000 | 0.0000000 | 4 | Bacteria | Actinobacteria | Actinobacteria | Micrococcales | Micrococcaceae | Ambiguous_taxa | Ambiguous_taxa |
| OTU.9 | 12C-Ami | agriculture | Yes | 2031.842266 | 2.6071517 | 0.2382931 | 9.891816 | 0.0000000 | 0.0000000 | 2 | Bacteria | Actinobacteria | Actinobacteria | Micrococcales | Micrococcaceae | Ambiguous_taxa | Ambiguous_taxa |
| OTU.9 | 12C-Ami | agriculture | Yes | 1402.221020 | 2.2974544 | 0.2917992 | 7.016655 | 0.0000000 | 0.0000000 | 4 | Bacteria | Actinobacteria | Actinobacteria | Micrococcales | Micrococcaceae | Ambiguous_taxa | Ambiguous_taxa |
| OTU.9 | 12C-Ami | agriculture | Yes | 2031.842266 | 2.6071517 | 0.2382931 | 9.891816 | 0.0000000 | 0.0000000 | 2 | Bacteria | Actinobacteria | Actinobacteria | Micrococcales | Micrococcaceae | Ambiguous_taxa | Ambiguous_taxa |
| OTU.315 | 12C-Ami | agriculture | Yes | 392.139985 | 1.9600863 | 0.2492455 | 6.861052 | 0.0000000 | 0.0000000 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Massilia | uncultured bacterium |
| OTU.315 | 12C-Ami | agriculture | Yes | 535.845657 | 2.2230735 | 0.2927950 | 6.738753 | 0.0000000 | 0.0000000 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Massilia | uncultured bacterium |
| OTU.315 | 12C-Ami | agriculture | Yes | 392.139985 | 1.9600863 | 0.2492455 | 6.861052 | 0.0000000 | 0.0000000 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Massilia | uncultured bacterium |
| OTU.315 | 12C-Ami | agriculture | Yes | 535.845657 | 2.2230735 | 0.2927950 | 6.738753 | 0.0000000 | 0.0000000 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Massilia | uncultured bacterium |
| OTU.23 | 12C-Ami | agriculture | Yes | 93.608292 | 1.5439977 | 0.2326064 | 5.563036 | 0.0000000 | 0.0000015 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Ralstonia | NA |
| OTU.20 | 12C-Ami | agriculture | Yes | 757.867525 | 0.7834658 | 0.1667190 | 3.199790 | 0.0006876 | 0.0303748 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Ambiguous_taxa | Ambiguous_taxa |
| OTU.463 | 12C-Ami | agriculture | Yes | 148.253559 | 2.4180415 | 0.3429677 | 6.321416 | 0.0000000 | 0.0000000 | 4 | Bacteria | Actinobacteria | Actinobacteria | Micrococcales | Micrococcaceae | Ambiguous_taxa | Ambiguous_taxa |
| OTU.463 | 12C-Ami | agriculture | Yes | 245.424035 | 2.6656187 | 0.3248678 | 7.435699 | 0.0000000 | 0.0000000 | 2 | Bacteria | Actinobacteria | Actinobacteria | Micrococcales | Micrococcaceae | Ambiguous_taxa | Ambiguous_taxa |
| OTU.463 | 12C-Ami | agriculture | Yes | 148.253559 | 2.4180415 | 0.3429677 | 6.321416 | 0.0000000 | 0.0000000 | 4 | Bacteria | Actinobacteria | Actinobacteria | Micrococcales | Micrococcaceae | Ambiguous_taxa | Ambiguous_taxa |
| OTU.463 | 12C-Ami | agriculture | Yes | 245.424035 | 2.6656187 | 0.3248678 | 7.435699 | 0.0000000 | 0.0000000 | 2 | Bacteria | Actinobacteria | Actinobacteria | Micrococcales | Micrococcaceae | Ambiguous_taxa | Ambiguous_taxa |
| OTU.1386 | 12C-Ami | agriculture | Yes | 58.258922 | 1.2905180 | 0.2966817 | 3.507186 | 0.0002264 | 0.0125672 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | NA | NA |
| OTU.1386 | 12C-Ami | agriculture | Yes | 66.543042 | 2.1527448 | 0.2855800 | 6.662739 | 0.0000000 | 0.0000000 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | NA | NA |
| OTU.1386 | 12C-Ami | agriculture | Yes | 58.258922 | 1.2905180 | 0.2966817 | 3.507186 | 0.0002264 | 0.0125672 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | NA | NA |
| OTU.1386 | 12C-Ami | agriculture | Yes | 66.543042 | 2.1527448 | 0.2855800 | 6.662739 | 0.0000000 | 0.0000000 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | NA | NA |
| OTU.2 | 12C-Xyl | forest | Yes | 2283.299024 | 1.2753339 | 0.2374383 | 4.318317 | 0.0000079 | 0.0059549 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.1 | 12C-Xyl | forest | Yes | 354.827089 | 0.9391310 | 0.1456613 | 4.731050 | 0.0000011 | 0.0016920 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | Ambiguous_taxa |
| OTU.66 | 12C-Van | forest | Yes | 64.385275 | 1.5741686 | 0.2865746 | 4.620677 | 0.0000019 | 0.0026750 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.2 | 12C-Van | forest | Yes | 3957.520597 | 1.7175488 | 0.3265153 | 4.494579 | 0.0000035 | 0.0026750 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.2 | 12C-Van | forest | Yes | 4835.350981 | 1.4233702 | 0.2440426 | 4.808054 | 0.0000008 | 0.0012177 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.2 | 12C-Van | forest | Yes | 3957.520597 | 1.7175488 | 0.3265153 | 4.494579 | 0.0000035 | 0.0026750 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.2 | 12C-Van | forest | Yes | 4835.350981 | 1.4233702 | 0.2440426 | 4.808054 | 0.0000008 | 0.0012177 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.951 | 12C-Xyl | meadow | Yes | 23.722085 | 1.4350722 | 0.2565998 | 4.618367 | 0.0000019 | 0.0003055 | 2 | Bacteria | Actinobacteria | Actinobacteria | Micrococcales | Microbacteriaceae | NA | NA |
| OTU.9 | 12C-Xyl | meadow | Yes | 307.578454 | 1.5465536 | 0.2465028 | 5.259791 | 0.0000001 | 0.0000171 | 2 | Bacteria | Actinobacteria | Actinobacteria | Micrococcales | Micrococcaceae | Ambiguous_taxa | Ambiguous_taxa |
| OTU.154 | 12C-Xyl | meadow | Yes | 110.673703 | 1.4643399 | 0.2625814 | 4.624622 | 0.0000019 | 0.0003055 | 2 | Bacteria | Actinobacteria | Actinobacteria | Micrococcales | Cellulomonadaceae | Cellulomonas | Ambiguous_taxa |
| OTU.2 | 12C-Xyl | meadow | Yes | 180.311952 | 1.7821720 | 0.2239602 | 6.841270 | 0.0000000 | 0.0000000 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.66 | 12C-Xyl | meadow | Yes | 28.053223 | 2.0078524 | 0.2912671 | 6.035190 | 0.0000000 | 0.0000003 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.2396 | 12C-Xyl | meadow | Yes | 19.764453 | 1.6472424 | 0.3547402 | 3.938777 | 0.0000409 | 0.0055457 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Massilia | NA |
| OTU.187 | 12C-Xyl | meadow | Yes | 51.968729 | 1.6904341 | 0.2269908 | 6.345781 | 0.0000000 | 0.0000001 | 2 | Bacteria | Proteobacteria | Alphaproteobacteria | Rhizobiales | Rhizobiaceae | Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium | uncultured bacterium |
| OTU.2396 | 12C-Ami | agriculture | Yes | 27.086295 | 1.8923076 | 0.3811749 | 4.308541 | 0.0000082 | 0.0006384 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Massilia | NA |
| OTU.2396 | 12C-Ami | agriculture | Yes | 43.956143 | 1.8111898 | 0.4107461 | 3.800863 | 0.0000721 | 0.0043848 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Massilia | NA |
| OTU.2396 | 12C-Ami | agriculture | Yes | 27.086295 | 1.8923076 | 0.3811749 | 4.308541 | 0.0000082 | 0.0006384 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Massilia | NA |
| OTU.2396 | 12C-Ami | agriculture | Yes | 43.956143 | 1.8111898 | 0.4107461 | 3.800863 | 0.0000721 | 0.0043848 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Massilia | NA |
| OTU.8 | 12C-Van | agriculture | Yes | 1850.903401 | 2.3176915 | 0.3966413 | 5.213001 | 0.0000001 | 0.0000388 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Massilia | Ambiguous_taxa |
| OTU.2396 | 12C-Van | agriculture | Yes | 90.659803 | 3.0985649 | 0.4914833 | 5.795852 | 0.0000000 | 0.0000028 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Massilia | NA |
| OTU.2 | 12C-Van | agriculture | Yes | 2180.028131 | 2.1732379 | 0.4791039 | 4.014240 | 0.0000298 | 0.0181100 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.2 | 12C-Van | agriculture | Yes | 1069.948292 | 2.3885562 | 0.4823726 | 4.433412 | 0.0000046 | 0.0012908 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.2 | 12C-Van | agriculture | Yes | 2180.028131 | 2.1732379 | 0.4791039 | 4.014240 | 0.0000298 | 0.0181100 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.2 | 12C-Van | agriculture | Yes | 1069.948292 | 2.3885562 | 0.4823726 | 4.433412 | 0.0000046 | 0.0012908 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.1 | 12C-Van | agriculture | Yes | 980.734255 | 2.6471368 | 0.5128590 | 4.674066 | 0.0000015 | 0.0013451 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | Ambiguous_taxa |
| OTU.1 | 12C-Van | agriculture | Yes | 589.750952 | 3.0252483 | 0.5020376 | 5.527970 | 0.0000000 | 0.0000090 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | Ambiguous_taxa |
| OTU.1 | 12C-Van | agriculture | Yes | 980.734255 | 2.6471368 | 0.5128590 | 4.674066 | 0.0000015 | 0.0013451 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | Ambiguous_taxa |
| OTU.1 | 12C-Van | agriculture | Yes | 589.750952 | 3.0252483 | 0.5020376 | 5.527970 | 0.0000000 | 0.0000090 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | Ambiguous_taxa |
| OTU.8 | 12C-Xyl | agriculture | Yes | 1616.901682 | 1.2408664 | 0.2885439 | 3.434023 | 0.0002973 | 0.0098445 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Massilia | Ambiguous_taxa |
| OTU.131 | 12C-Pal | agriculture | Yes | 43.772174 | 1.5132675 | 0.3099683 | 4.075473 | 0.0000230 | 0.0165366 | 28 | Bacteria | Actinobacteria | Actinobacteria | Corynebacteriales | Nocardiaceae | Nocardia | NA |
| OTU.56 | 12C-Van | agriculture | Yes | 193.226833 | 2.0932142 | 0.3779803 | 4.876482 | 0.0000005 | 0.0001804 | 4 | Bacteria | Proteobacteria | Alphaproteobacteria | Rhizobiales | Beijerinckiaceae | Methylobacterium | uncultured bacterium |
| OTU.1541 | 12C-Xyl | agriculture | Yes | 63.550455 | 1.1522105 | 0.2627946 | 3.433139 | 0.0002983 | 0.0098445 | 2 | Bacteria | Actinobacteria | Actinobacteria | Micrococcales | Microbacteriaceae | Microbacterium | Ambiguous_taxa |
| OTU.9 | 12C-Xyl | agriculture | Yes | 823.875478 | 1.4954986 | 0.1899400 | 6.557326 | 0.0000000 | 0.0000000 | 2 | Bacteria | Actinobacteria | Actinobacteria | Micrococcales | Micrococcaceae | Ambiguous_taxa | Ambiguous_taxa |
| OTU.18 | 12C-Cel | agriculture | Yes | 83.450918 | 1.4174655 | 0.2508977 | 4.653154 | 0.0000016 | 0.0032068 | 14 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Duganella | NA |
| OTU.3 | 12C-Van | agriculture | Yes | 1596.345820 | 3.3653851 | 0.4892176 | 6.368097 | 0.0000000 | 0.0000002 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Pseudomonadales | Pseudomonadaceae | Pseudomonas | Ambiguous_taxa |
| OTU.463 | 12C-Xyl | agriculture | No | 57.143056 | 1.7055406 | 0.3470587 | 4.193932 | 0.0000137 | 0.0118094 | 4 | Bacteria | Actinobacteria | Actinobacteria | Micrococcales | Micrococcaceae | Ambiguous_taxa | Ambiguous_taxa |
| OTU.463 | 12C-Xyl | agriculture | No | 176.220818 | 2.3651105 | 0.3061382 | 6.909005 | 0.0000000 | 0.0000000 | 2 | Bacteria | Actinobacteria | Actinobacteria | Micrococcales | Micrococcaceae | Ambiguous_taxa | Ambiguous_taxa |
| OTU.951 | 12C-Xyl | agriculture | No | 84.136147 | 0.8679262 | 0.2059635 | 3.000173 | 0.0013491 | 0.0400692 | 2 | Bacteria | Actinobacteria | Actinobacteria | Micrococcales | Microbacteriaceae | NA | NA |
| OTU.154 | 12C-Xyl | agriculture | No | 83.888332 | 1.5065962 | 0.2482874 | 5.061054 | 0.0000002 | 0.0000155 | 2 | Bacteria | Actinobacteria | Actinobacteria | Micrococcales | Cellulomonadaceae | Cellulomonas | Ambiguous_taxa |
| OTU.315 | 12C-Xyl | agriculture | No | 477.770963 | 1.5345565 | 0.3461871 | 3.710585 | 0.0001034 | 0.0051178 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Massilia | uncultured bacterium |
| OTU.187 | 12C-Xyl | agriculture | No | 64.905291 | 1.6165815 | 0.2975099 | 4.593398 | 0.0000022 | 0.0001295 | 2 | Bacteria | Proteobacteria | Alphaproteobacteria | Rhizobiales | Rhizobiaceae | Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium | uncultured bacterium |
| OTU.8 | 12C-Ami | agriculture | No | 1100.658842 | 1.6892232 | 0.2190543 | 6.570168 | 0.0000000 | 0.0000000 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Massilia | Ambiguous_taxa |
| OTU.4461 | 12C-Ami | agriculture | No | 24.743522 | 1.5908981 | 0.3615954 | 3.708283 | 0.0001043 | 0.0067557 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | NA | NA |
| OTU.7776 | 12C-Ami | agriculture | No | 115.221961 | 0.8563077 | 0.1714353 | 3.536657 | 0.0002026 | 0.0121100 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | NA | NA |
| OTU.3 | 12C-Ami | agriculture | No | 31.418578 | 2.3544258 | 0.4180531 | 5.033872 | 0.0000002 | 0.0000233 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Pseudomonadales | Pseudomonadaceae | Pseudomonas | Ambiguous_taxa |
| OTU.3589 | 12C-Ami | agriculture | No | 14.800817 | 1.5392306 | 0.4049181 | 3.183929 | 0.0007265 | 0.0303748 | 2 | Bacteria | Bacteroidetes | Bacteroidia | Chitinophagales | Chitinophagaceae | Flavisolibacter | NA |
| OTU.8 | 12C-Ami | agriculture | No | 1912.137734 | 1.8269501 | 0.2647118 | 5.957233 | 0.0000000 | 0.0000001 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Massilia | Ambiguous_taxa |
| OTU.2513 | 12C-Ami | agriculture | No | 62.213661 | 1.1788555 | 0.2616336 | 3.550216 | 0.0001925 | 0.0091967 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | NA | NA |
| OTU.4461 | 12C-Ami | agriculture | No | 32.142982 | 1.6209049 | 0.3337316 | 4.107807 | 0.0000200 | 0.0013361 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | NA | NA |
| OTU.3 | 12C-Ami | agriculture | No | 113.241648 | 2.8100466 | 0.4301395 | 5.951666 | 0.0000000 | 0.0000001 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Pseudomonadales | Pseudomonadaceae | Pseudomonas | Ambiguous_taxa |
| OTU.711 | 12C-Ami | agriculture | No | 32.047650 | 1.3359246 | 0.3043876 | 3.567572 | 0.0001802 | 0.0091967 | 2 | Bacteria | Proteobacteria | Alphaproteobacteria | Rhizobiales | Beijerinckiaceae | Bosea | uncultured bacterium |
| OTU.187 | 12C-Ami | agriculture | No | 53.188007 | 1.0761760 | 0.2623858 | 3.148707 | 0.0008200 | 0.0322683 | 2 | Bacteria | Proteobacteria | Alphaproteobacteria | Rhizobiales | Rhizobiaceae | Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium | uncultured bacterium |
| OTU.66 | 12C-Van | agriculture | No | 211.157488 | 2.0560193 | 0.4797711 | 3.764335 | 0.0000835 | 0.0380326 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.66 | 12C-Van | agriculture | No | 167.914591 | 2.7058574 | 0.3884788 | 6.321728 | 0.0000000 | 0.0000002 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.18 | 12C-Pal | agriculture | No | 42.941360 | 1.4081744 | 0.2860221 | 4.049247 | 0.0000257 | 0.0165366 | 28 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Duganella | NA |
| OTU.4420 | 12C-Pal | agriculture | No | 113.862921 | 2.3114293 | 0.3187109 | 6.468023 | 0.0000000 | 0.0000001 | 28 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Aquabacterium | NA |
| OTU.1863 | 12C-Ami | meadow | No | 63.905406 | 1.7674692 | 0.3457300 | 4.389175 | 0.0000057 | 0.0013464 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Massilia | NA |
| OTU.315 | 12C-Ami | meadow | No | 168.680186 | 1.4360614 | 0.2688758 | 4.411186 | 0.0000051 | 0.0013464 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Massilia | uncultured bacterium |
| OTU.66 | 12C-Van | meadow | No | 45.834871 | 1.7014041 | 0.3599458 | 4.032285 | 0.0000276 | 0.0104398 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.2 | 12C-Van | meadow | No | 828.031945 | 2.7981725 | 0.2536109 | 10.047569 | 0.0000000 | 0.0000000 | 4 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.29 | 12C-Van | meadow | No | 23.315455 | 1.6340944 | 0.3734184 | 3.706551 | 0.0001051 | 0.0317673 | 4 | Bacteria | Proteobacteria | Alphaproteobacteria | Sphingomonadales | Sphingomonadaceae | Sphingomonas | Ambiguous_taxa |
| OTU.85 | 12C-Van | meadow | No | 50.025534 | 1.4205890 | 0.3140419 | 3.727494 | 0.0000967 | 0.0070973 | 2 | Bacteria | Firmicutes | Bacilli | Bacillales | Bacillaceae | Bacillus | NA |
| OTU.35 | 12C-Van | meadow | No | 83.736607 | 1.1173289 | 0.2567176 | 3.378533 | 0.0003644 | 0.0202832 | 2 | Bacteria | Firmicutes | Bacilli | Bacillales | Bacillaceae | Bacillus | uncultured bacterium |
| OTU.2977 | 12C-Van | meadow | No | 43.992439 | 1.4438190 | 0.3223333 | 3.703679 | 0.0001062 | 0.0070973 | 2 | Bacteria | Firmicutes | Bacilli | Bacillales | Bacillaceae | Bacillus | Ambiguous_taxa |
| OTU.98 | 12C-Van | meadow | No | 26.822922 | 1.5949465 | 0.3292538 | 4.084832 | 0.0000221 | 0.0019933 | 2 | Bacteria | Firmicutes | Bacilli | Bacillales | Bacillaceae | Bacillus | Ambiguous_taxa |
| OTU.2396 | 12C-Van | meadow | No | 50.033273 | 2.6333978 | 0.3383678 | 7.043808 | 0.0000000 | 0.0000000 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Massilia | NA |
| OTU.66 | 12C-Van | meadow | No | 46.853527 | 2.5619807 | 0.3194617 | 7.237114 | 0.0000000 | 0.0000000 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.2 | 12C-Van | meadow | No | 818.088037 | 2.2775018 | 0.3727713 | 5.438996 | 0.0000000 | 0.0000036 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Betaproteobacteriales | Burkholderiaceae | Burkholderia-Caballeronia-Paraburkholderia | NA |
| OTU.16149 | 12C-Van | meadow | No | 12.255583 | 1.6074319 | 0.4011831 | 3.383572 | 0.0003577 | 0.0202832 | 2 | Bacteria | Proteobacteria | Gammaproteobacteria | Pseudomonadales | Pseudomonadaceae | Pseudomonas | Pseudomonas koreensis |
| OTU.9 | 12C-Ami | forest | No | 279.245634 | 1.2881788 | 0.1957360 | 5.303976 | 0.0000001 | 0.0000441 | 2 | Bacteria | Actinobacteria | Actinobacteria | Micrococcales | Micrococcaceae | Ambiguous_taxa | Ambiguous_taxa |